Acoustic characterization of bats from Malta: setting a baseline for monitoring and conservation of bat populations

Bioacoustic research has made several advancements in developing systems to record extensive acoustic data and classify bat echolocation calls to species level using automated classifiers. These systems are useful as echolocation calls give valuable information on bat behaviour and ecology and hence are widely used for research and conservation of bat populations. Despite the challenges associated with automated classifiers, due to the interspecific differences in call characteristics of bat species found in the Maltese Islands, the use of a quantitative and automated approach is investigated. The sound analysis pipeline involved the use of an algorithm to clean sound files from background noise and measure temporal and spectral parameters of bat echolocation calls. These parameters were then fed to a trained and validated artificial neural network using a bat call library built from reference bat calls from Malta. The automatic classifier achieved an overall correct classification rate of 98%. This high correct classification rate for reliable species identification may have benefitted from the absence of typically problematic species, such as species in the genus Myotis, in the analyses. This study’s results pave the way for efficient and reliable bat acoustic surveys in Malta in aid of necessary monitoring and conservation by providing an updated bat species list and their echolocation characteristics.     

Automated classification of bat echolocation call recordings with artificial intelligence

Acoustic recorders are commonly used to remotely monitor and collect data on bats (Order Chiroptera). These efforts result in many acoustic recordings that must be classified by a bat biologist with expertise in call classification in order to obtain useful information. The rarity of this expertise and time constraints have prompted efforts to automatically classify bat species in acoustic recordings using a variety of learning methods. There are several software programs available for this purpose, but they are imperfect and the United States Fish and Wildlife Service often recommends that a qualified acoustic analyst review bat call identifications even if using these software programs. We sought to build a model to classify bat species using modern computer vision techniques. We used images of bat echolocation calls (i.e., plots of the pulses) to train deep learning computer vision models that automatically classify bat calls to species. Our model classifies 10 species, five of which are protected under the Endangered Species Act. We evaluated our models using standard model validation procedures, and performed two external tests. For these tests, an entire dataset was withheld from the procedure before splitting the data into training and validation sets. We found that our validation accuracy (92%) and testing accuracy (90%) were higher than when we used Kaleidoscope Pro and BCID software (65% and 61% accuracy, respectively) to evaluate the same calls. Our results suggest that our approach is effective at classifying bat species from acoustic recordings, and our trained model will be incorporated into new bat call identification software: WEST-EchoVision.     

Acoustic identification of bats in the eastern United States: A comparison of parametric and nonparametric methods

Ultrasonic detectors are widely used to survey bats in ecological studies. To evaluate efficacy of acoustic identification, we compiled a library of search phase calls from across the eastern United States using the Anabat system. The call library included 1,846 call sequences of 12 species recorded from 14 states. We determined accuracy rates using 3 parametric and 4 nonparametric classification functions for acoustic identification. The 2 most flexible classification functions also were the most accurate: neural networks (overall classification accuracy = 0.94) and mixture discriminant analysis incorporating an adaptive regression model (overall classification accuracy = 0.93). Flexible nonparametric methods offer substantial benefits when discriminating among closely related species and may preclude the need to group species with similar calls. We demonstrate that quantitative methods provide an effective technique to acoustically identify bats in the eastern United States with known accuracy rates. © 2011 The Wildlife Society.     

A novel approach to field identification of cryptic Apodemus wood mice: calls differ more than morphology

Field identification of European wood mice Apodemus spp. is challenging due to their morphological resemblance and frequent sympatry. We developed discriminant functions based on body mass and acoustic variables of distress calls to identify three cryptic species of wood mice (Apodemus alpicola, Apodemus flavicollis and Apodemus sylvaticus) in Italy. We achieved an overall correct classification rate of 86–98%; the best results (100% correct classification) were obtained for Apodemus sylvaticus calls. This minimally invasive, effective and low‐cost method highlights the potential role of bioacoustics as a powerful tool for field discrimination of cryptic species of terrestrial mammals.     

Acoustic classification of dolphins in the California Current using whistles,echolocation clicks,and burst pulses

Passive acoustic monitoring of dolphins is limited by our ability to classify calls to species. Significant overlap in call characteristics among many species, combined with a wide range of call types and acoustic behavior, makes classification of calls to species challenging. Here, we introduce BANTER, a compound acoustic classification method for dolphins that utilizes information from all call types produced by dolphins rather than a single call type, as has been typical for acoustic classifiers. Output from the passive acoustic monitoring software, PAMGuard, was used to create independent classifiers for whistles, echolocation clicks, and burst pulses, which were then merged into a final, compound classifier for each species. Classifiers for five species found in the California Current ecosystem were trained and tested using 153 single‐species acoustic events recorded during a 4.5 mo combined visual and acoustic shipboard cetacean survey off the west coast of the United States. Correct classification scores for individual species ranged from 71% to 92%, with an overall correct classification score of 84% for all five species. The conceptual framework of this approach easily lends itself to other species and study areas as well as to noncetacean taxa.     

Animal Sound Identifier (ASI): software for automated identification of vocal animals

Automated audio recording offers a powerful tool for acoustic monitoring schemes of bird, bat, frog and other vocal organisms, but the lack of automated species identification methods has made it difficult to fully utilise such data. We developed Animal Sound Identifier (ASI), a MATLAB software that performs probabilistic classification of species occurrences from field recordings. Unlike most previous approaches, ASI locates training data directly from the field recordings and thus avoids the need of pre‐defined reference libraries. We apply ASI to a case study on Amazonian birds, in which we classify the vocalisations of 14 species in 194 504 one‐minute audio segments using in total two weeks of expert time to construct, parameterise, and validate the classification models. We compare the classification performance of ASI (with training templates extracted automatically from field data) to that of monitoR (with training templates extracted manually from the Xeno‐Canto database), the results showing ASI to have substantially higher recall and precision rates.     

The use of automated identification of bat echolocation calls in acoustic monitoring: A cautionary note for a sound analysis

Bats are a species-rich order of mammals providing key ecosystem services. Because bats are threatened by human action and also serve as important bioindicators, monitoring their populations is of utmost importance. However, surveying bats is difficult because of their nocturnal habits, elusiveness and sensitivity to disturbance. Bat detectors allow echolocating bats to be surveyed non-invasively and record species that would otherwise be difficult to observe by capture or roost inspection. Unfortunately, several bat species cannot be identified confidently from their calls so acoustic classification remains ambiguous or impossible in some cases.The popularity of automated classifiers of bat echolocation calls has escalated rapidly, including that of several packages available on purchase. Such products have filled a vacant niche on the market mostly in relation to the expanding monitoring efforts related to the development of wind energy production worldwide.We highlight that no classifier has yet proven capable of providing correct classifications in 100% of cases or getting close enough to this ideal performance. Besides, from the literature available and our own experience we argue that such tools have not yet been tested sufficiently in the field. Visual inspection of calls whose automated classification is judged suspicious is often recommended, but human intervention a posteriori represents a circular argument and requires noticeable experience.We are concerned that neophytes – including consultants with little experience with bats but specialized into other taxonomical groups – will accept passively automated responses of tools still awaiting sufficient validation. We remark that bat call identification is a serious practical issue because biases in the assessment of bat distribution or habitat preferences may lead to wrong management decisions with serious conservation consequences. Automated classifiers may crucially aid bat research and certainly merit further investigations but the boost in commercially available software may have come too early. Thorough field tests need to be carried out to assess limitations and strengths of these tools.     

Decoding the ‘zeep’ complex: quantitative analysis of interspecific variation in the nocturnal flight calls of nine wood warbler species (Parulidae spp.)

ABSTRACT

The “zeep” complex consists of nine birds that produce nocturnal flight calls with similar acoustic features. Our inability to distinguish these calls inhibits the acoustic monitoring of these species. We test the hypothesis that flight calls of nine warblers in the “zeep” complex show sufficient acoustic differences to allow differentiation. We investigate divergence in these vocalizations by recording birds held for banding and collecting additional recordings from sound libraries. We used three approaches to compare calls between species: analysis of variance in acoustic properties, discriminant analysis of acoustic properties, and spectrographic cross-correlation. The first approach revealed five species that were different in one or more acoustic properties. The second approach revealed a level of assignment to the correct species (73%) that exceeded levels expected by chance (36%). The third approach revealed calls of seven species to be significantly more similar to conspecific calls than heterospecific calls. Our results suggest the calls of many members of the “zeep” complex exhibit species-specific differences in structure, which may allow differentiation of at least five “zeep” species based on call alone. We advocate for the combined use of these three approaches for the comparison of “zeep” calls in future flight call studies.     

Testing the performances of automated identification of bat echolocation calls: A request for prudence

Echolocating bats are surveyed and studied acoustically with bat detectors routinely and worldwide, yet identification of species from calls often remains ambiguous or impossible due to intraspecific call variation and/or interspecific overlap in call design. To overcome such difficulties and to reduce workload, automated classifiers of echolocation calls have become popular, but their performance has not been tested sufficiently in the field. We examined the absolute performance of two commercially available programs (SonoChiro and Kaleidoscope) and one freeware package (BatClassify). We recorded noise from rain and calls of seven common bat species with Pettersson real-time full spectrum detectors in Sweden. The programs could always (100%) distinguish rain from bat calls, usually (68–100%) identify bats to group (Nyctalus/Vespertilio/Eptesicus, Pipistrellus, Myotis, Plecotus, Barbastella) and usually (83–99%) recognize typical calls of some species whose echolocation pulses are structurally distinct (Pipistrellus pygmaeus, Barbastella barbastellus). Species with less characteristic echolocation calls were not identified reliably, including Vespertilio murinus (16–26%), Myotis spp. (4–93%) and Plecotus auritus (0–89%). All programs showed major although different shortcomings and the often poor performance raising serious concerns about the use of automated classifiers for identification to species level in research and surveys. We highlight the importance of validating output from automated classifiers, and restricting their use to specific situations where identification can be made with high confidence. For comparison we also present the result of a manual identification test on a random subset of the files used to test the programs. It showed a higher classification success but performances were still low for more problematic taxa.     

A comparison of supervised learning techniques in the classification of bat echolocation calls

Today's acoustic monitoring devices are capable of recording and storing tremendous amounts of data. Until recently, the classification of animal vocalizations from field recordings has been relegated to qualitative approaches. For large-scale acoustic monitoring studies, qualitative approaches are very time-consuming and suffer from the bias of subjectivity. Recent developments in supervised learning techniques can provide rapid, accurate, species-level classification of bioacoustics data. We compared the classification performances of four supervised learning techniques (random forests, support vector machines, artificial neural networks, and discriminant function analysis) for five different classification tasks using bat echolocation calls recorded by a popular frequency-division bat detector. We found that all classifiers performed similarly in terms of overall accuracy with the exception of discriminant function analysis, which had the lowest average performance metrics. Random forests had the advantage of high sensitivities, specificities, and predictive powers across the majority of classification tasks, and also provided metrics for determining the relative importance of call features in distinguishing between groups. Overall classification accuracy for each task was slightly lower than reported accuracies using calls recorded by time-expansion detectors. Myotis spp. were particularly difficult to separate; classifiers performed best when members of this genus were combined in genus-level classification and analyzed separately at the level of species. Additionally, we identified and ranked the relative contributions of all predictor features to classifier accuracy and found measurements of frequency, total call duration, and characteristic slope to be the most important contributors to classification success. We provide recommendations to maximize accuracy and efficiency when analyzing acoustic data, and suggest an application of automated bioacoustics monitoring to contribute to wildlife monitoring efforts.     

Stronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys

Owing to major technological advances, bioacoustics has become a burgeoning field in ecological research worldwide. Autonomous passive acoustic recorders are becoming widely used to monitor aerial insectivorous bats, and automatic classifiers have emerged to aid researchers in the daunting task of analysing the resulting massive acoustic datasets. However, the scarcity of comprehensive reference call libraries still hampers their wider application in highly diverse tropical assemblages. Capitalizing on a unique acoustic dataset of >650,000 bat call sequences collected over a 3-year period in the Brazilian Amazon, the aims of this study were (a) to assess how pre-identified recordings of free-flying and hand-released bats could be used to train an automatic classification algorithm (random forest), and (b) to optimize acoustic analysis protocols by combining automatic classification with visual post-validation, whereby we evaluated the proportion of sound files to be post-validated for different thresholds of classification accuracy. Classifiers were trained at species or sonotype (group of species with similar calls) level. Random forest models confirmed the reliability of using calls of both free-flying and hand-released bats to train custom-built automatic classifiers. To achieve a general classification accuracy of ~85%, random forest had to be trained with at least 500 pulses per species/sonotype. For seven out of 20 sonotypes, the most abundant in our dataset, we obtained high classification accuracy (>90%). Adopting a desired accuracy probability threshold of 95% for the random forest classifier, we found that the percentage of sound files required for manual post-validation could be reduced by up to 75%, a significant saving in terms of workload. Combining automatic classification with manual ID through fully customizable classifiers implemented in open-source software as demonstrated here shows great potential to help overcome the acknowledged risks and biases associated with the sole reliance on automatic classification.     

Weather conditions determine attenuation and speed of sound: Environmental limitations for monitoring and analyzing bat echolocation

Echolocating bats are regularly studied to investigate auditory‐guided behaviors and as important bioindicators. Bioacoustic monitoring methods based on echolocation calls are increasingly used for risk assessment and to ultimately inform conservation strategies for bats. As echolocation calls transmit through the air at the speed of sound, they undergo changes due to atmospheric and geometric attenuation. Both the speed of sound and atmospheric attenuation, however, are variable and determined by weather conditions, particularly temperature and relative humidity. Changing weather conditions thus cause variation in analyzed call parameters, limiting our ability to detect, and correctly analyze bat calls. Here, I use real‐world weather data to exemplify the effect of varying weather conditions on the acoustic properties of air. I then present atmospheric attenuation and speed of sound for the global range of weather conditions and bat call frequencies to show their relative effects. Atmospheric attenuation is a nonlinear function of call frequency, temperature, relative humidity, and atmospheric pressure. While atmospheric attenuation is strongly positively correlated with call frequency, it is also significantly influenced by temperature and relative humidity in a complex nonlinear fashion. Variable weather conditions thus result in variable and unknown effects on the recorded call, affecting estimates of call frequency and intensity, particularly for high frequencies. Weather‐induced variation in speed of sound reaches up to about ±3%, but is generally much smaller and only relevant for acoustic localization methods of bats. The frequency‐ and weather‐dependent variation in atmospheric attenuation has a threefold effect on bioacoustic monitoring of bats: It limits our capability (1) to monitor bats equally across time, space, and species, (2) to correctly measure frequency parameters of bat echolocation calls, particularly for high frequencies, and (3) to correctly identify bat species in species‐rich assemblies or for sympatric species with similar call designs.     

Two‐component calls in short‐finned pilot whales (Globicephala macrorhynchus)

Short‐finned pilot whales (Globicephala macrorhynchus) have complex vocal repertoires that include calls with two time‐frequency contours known as two‐component calls. We attached digital acoustic recording tags (DTAGs) to 23 short‐finned pilot whales off Cape Hatteras, North Carolina, and assessed the similarity of two‐component calls within and among tags. Two‐component calls made up <3% of the total number of calls on 19 of the 23 tag records. For the remaining four tags, two‐component calls comprised 9%, 23%, 24%, and 57% of the total calls recorded. Measurements of six acoustic parameters for both the low and high frequency components of all two‐component calls from the five tags were compared using a generalized linear model. There were significant differences in the acoustic parameters of two‐component calls between tags, verifying that acoustic parameters were more similar for two‐component calls recorded on the same tag than for calls between tags. Spectrograms of all two‐component calls from the five tags were visually graded and independently categorized by five observers. A test of inter‐rater reliability showed substantial agreement, suggesting that each tag contained a predominant two‐component call type that was not shared across tags.     

Sex recognition in brown skuas: do acoustic signals matter?

Bird vocalisations are often essential for sex recognition, especially in species that show little morphological sex dimorphism. Brown skuas (Catharacta antarctica lonnbergi), which exhibit uniform plumage across both sexes, emit three main calls: the long call, the alarm call and the contact call. We tested the potential for sex recognition in brown skua calls of 42 genetically sexed individuals by analysing 8–12 acoustic parameters in the temporal and frequency domains of each call type. For every call type, we failed to find sex differences in any of the acoustic parameters measured. Stepwise discriminant function analysis (DFA) revealed that sexes cannot be unambiguously classified, with increasing uncertainty of correct classification from contact calls to long calls to alarm calls. Consequently, acoustic signalling is probably not the key mechanism for sex recognition in brown skuas.     

Habitat associations of bats in a working rangeland landscape

Land‐use change has resulted in rangeland loss and degradation globally. These changes include conversion of native grasslands for row‐crop agriculture as well as degradation of remaining rangeland due to fragmentation and changing disturbance regimes. Understanding how these and other factors influence wildlife use of rangelands is important for conservation and management of wildlife populations. We investigated bat habitat associations in a working rangeland in southeastern North Dakota. We used Petterson d500x acoustic detectors to systematically sample bat activity across the study area on a 1‐km point grid. We identified calls using Sonobat autoclassification software. We detected five species using this working rangeland, which included Lasionycteris noctivagans (2,722 detections), Lasiurus cinereus (2,055 detections), Eptesicus fuscus (749 detections), Lasiurus borealis (62 detections), and Myotis lucifugus (1 detection). We developed generalized linear mixed‐effects models for the four most frequently detected species based on their ecology. The activity of three bat species increased with higher tree cover. While the scale of selection varied between the four species, all three investigated scales were explanatory for at least one bat species. The broad importance of trees to bats in rangelands may put their conservation needs at odds with those of obligate grassland species. Focusing rangeland bat conservation on areas that were treed prior to European settlement, such as riparian forests, can provide important areas for bat conservation while minimizing negative impacts on grassland species.     

Advertisement call variation and individual acoustic distinctiveness in the explosive breeding toad <Emphasis Type="Italic">Peltophryne cataulaciceps</Emphasis> (Anura: Bufonidae)

In this contribution, we offer new information about the advertisement call of Peltophryne cataulaciceps, an endemic toad species from Cuba and the smallest bufonid from the West Indies. We measured seven acoustic properties from 17 males and analyzed the variability at the within-individual and between-individual levels, using coefficients of variation, type II ANOVAs, and multivariate analysis. Dominant frequency was distinctly less variable within individuals than the rest of the acoustic properties; call rise time showed the highest variability. Variability between individuals was higher for pulse rate, call duration, and dominant frequency, and the CVb/CVw ratios showed that these acoustic properties are more reliable for individual distinctiveness. Discriminant function analyses assigned 54.1% of the calls to the correct individual, and this classification success increased when smaller groups of individuals were considered in the analysis. Results are compared with studies addressing individual acoustic distinctiveness in anurans. We support that the patterns of advertisement call variation within and among co-occurring males differ among explosive and prolonged breeding species/populations, but additional case studies including other explosive breeding species are needed.     

A comparison of individual distinctiveness in three vocalizations of the dwarf mongoose (Helogale parvula)

Individual specificity can be found in the vocalizations of many avian and mammalian species. However, it is often difficult to determine whether these vocal cues to identity rise from “unselected” individual differences in vocal morphology or whether they have been accentuated by selection for the purposes of advertising caller identity. By comparing the level of acoustic individuality of different vocalizations within the repertoire of a single species, it is possible to ascertain whether selection for individual recognition has modified the vocal cues to identity in particular contexts. We used discriminant function analyses to determine the level of accuracy with which calls could be classified to the correct individual caller, for three dwarf mongoose (Helogale parvula) vocalizations: contact, snake, and isolation calls. These calls were similar in acoustic structure but divergent in context and function. We found that all three call types showed individual specificity but levels varied with call type (increasing from snake to contact to isolation call). The individual distinctiveness of each call type appeared to be directly related to the degree of benefit that signalers were likely to accrue from advertising their identity within that call context. We conclude that dwarf mongoose signalers have undergone selection to facilitate vocal individual recognition, particularly in relation to the species’ isolation call.     

Plasticity in echolocation calls of <Emphasis Type="Italic">Myotis macrodactylus</Emphasis> (Chiroptera: Vespertilionidae): implications for acoustic identification

Poor knowledge of the intraspecific variability in echolocation calls is recognized as an important limiting factor for the accurate acoustic identification of bats. We studied the echolocation behaviors of an ecologically poorly known bat species, Myotis macrodactylus, while they were commuting in three types of habitats differing significantly in the amount of background clutter, as well as searching for prey above the water surface in a river. Results showed that M. macrodactylus altered their echolocation call structure in the same way during commuting as foraging bats do in relation to the changing level of clutter. With increasing level of clutter, M. macrodactylus generally produced echolocation calls with higher start, end, and peak frequencies; wider bandwidth; and shorter pulse duration. Compared to commuting, bats emitted significantly lower frequency calls with narrower bandwidth while searching for prey. Discriminant function analysis indicated that 79.8% of the calls from the three commuting habitats were correctly grouped, and 87% of the calls were correctly classified to the commuting and foraging contexts. Our finding has implications for those who would identify species by their calls.     

Echolocation call description of 15 species of Middle-Eastern desert dwelling insectivorous bats

Modern advances in acoustic technology have made possible new and broad ranges of research in bioacoustics, particularly with regard to echolocating bats. In the present study, we present an acoustic guide to the calls of 15 species of bats in the Arava rift valley, Israel, with a focus on their bioacoustics, habitat use and explaining differences between similar species. We also describe a potential case of frequency separation where four bat species using six call types appear to separate the frequencies of their calls to minimize overlap. The studied community of bat species is also found in other Middle Eastern deserts including the deserts of Jordan, Syria and Saudi Arabia and we hope that data gathered will benefit other bat researchers in the region.